gRPC vs Kafka

Overview

When comparing gRPC and Kafka, we're likely comparing gRPC's streaming features. gRPC's unary RPC takes either asynchronous or synchronous forms of communication (in a Request/Response architectural paradigm). Kafka and gRPC Streaming are both examples of Event-Driven Architecture. There's typically an expectation of an immediate response in a Request/Response journey, whereas Event-Driven request journeys are typically send-and-forget (with journey durations anywhere from milliseconds to days in distributed systems). Acknowledgements of a triggered Event-Driven journey are common (as well as follow-up event responses).

High-Level Guidance

• If latency is the most important factor, choose gRPC Streaming: gRPC Streaming doesn’t require anything like Kafka’s intermediary message broker between a client and server. Its streams are direct between client and server, minimizing latency.
• If reliability out-of-the-box is the most important factor, choose Kafka: gRPC Streaming doesn’t provide application-level guaranteed delivery on its own. In contrast, Kafka not only guarantees message delivery at the application level, but also persists messages on its distributed message broker for a default of 14 days.

For more detail, continue reading:

Comparison

Note: Learn more about Throughput vs Latency.

Architectural Diagrams

Below are simplified diagrams illustrating the difference between Request/Response (gRPC) and Event-Driven (Kafka) approaches.

gRPC Streaming Flow

+----------+     (1) Request Stream    +----------+
| gRPC     |-------------------------> | gRPC     |
| Client   |                           | Server   |
+----------+                           +----------+
         ^                                |
         |      (2) Response Stream       |
         +--------------------------------+

• Direct communication between client and server.
• Low latency but ephemeral connections; if either side fails, the stream is lost.

Kafka Message Flow

+---------+  (1) Publish Messages  +--------+
| Producer| ---------------------> | Kafka  | -- (2) Stores Messages
| (Client)|                        | Broker |
+---------+                        +--------+
                                        \
                                         \
                                          v (3) Consume Messages
                                      +----------+
                                      | Consumer |
                                      +----------+

• Indirect communication with a broker in between.
• Messages persist for a configurable retention period; if a consumer is offline, it can catch up later.

gRPC Streaming

gRPC is touched on in gRPC vs REST. When comparing it to REST, we typically compare its unary RPC feature. However, when comparing it to Kafka, we compare its streaming features. There are three different streaming modes:

1. Server Streaming RPC: Server sends a stream of messages in response to a client's request.
2. Client Streaming RPC: Client sends a stream of messages to the server.
3. Bidirectional Streaming RPC: A two-way stream where both sides can send messages independently.

Kafka

Note that Kafka and Kafka Streams operate within the same infrastructure framework, but serve different purposes:

• Kafka: A distributed streaming platform that enables you to reliably stream messages from producers to consumers via an intermediary message broker in a highly fault-tolerant manner.
• Kafka Streams: Uses the same Kafka infrastructure, but provides a higher-level, more user-friendly client library to handle many technical details under-the-hood.

Advanced Trade-Offs

Below is a second table that delves deeper into operational and developer-experience aspects.

Conclusion

It’s unusual to introduce gRPC Streaming into a distributed system where Kafka (or another message broker) is widely used unless a use case arises where extremely low latency is of utmost importance (e.g., video/audio chat, real-time collaboration). On the other hand, gRPC Streaming is the better option if most of the following are true:

• The technical aptitude exists.
• The feature’s scale is not extremely high.
• The feature’s traffic is not highly variable.
• The overhead of introducing Kafka is too high.

In contrast, if your system demands guaranteed message persistence, higher variable throughput, and simpler consumer failover, Kafka is typically the better choice.

Quick Decision Flowchart

                      +-----------------------------------+
                      |   Do you need very low latency?   |
                      +-----------------------------------+
                                /              \
                               / YES            \ NO
                              v                  v
+--------------------------------------+   +--------------------------------------+
|  Do you also need guaranteed         |   | Is guaranteed message persistence    |
|  message persistence out-of-the-box? |   | or replay crucial for your use case? |
+---------+--------------+-------------+   +----------+--------------+------------+
       NO |              | YES                     NO |              | YES
          v              v                            v              v
Use gRPC Streaming    Use Kafka             Use gRPC Streaming    Use Kafka

Related blog posts:

• gRPC Interceptor
• gRPC vs REST
• Throughput vs Latency

Updated: 2025-01-04